考虑因果结构不确定性，提升决策可靠性
Incorporating structural uncertainty in causal decision making
- 用贝叶斯模型平均处理因果结构不确定问题
- 三种条件满足时，平均法显著优于传统方法
- 适合重视因果推断稳健性的研究者使用
基于因果效应做决策的实践者通常忽略结构不确定性。我们分析了该不确定性何时足以影响决策，需采用方法论解决方案（如对竞争因果结构进行贝叶斯模型平均）。聚焦双变量关系（X→Y 与 X←Y），证明当：(1) 结构不确定性中等到高，(2) 不同结构间因果效应差异显著，(3) 损失函数对因果效应大小敏感时，模型平均有益。在常规条件下，我们证明了该方法的最优性，并通过模拟表明，现代因果发现方法可在有限范围内提供必要的量化。本框架补充了现有鲁棒因果推断方法，解决了实践中常被忽视的结构性不确定性来源。
原文摘要 · Abstract (English)
Practitioners making decisions based on causal effects typically ignore structural uncertainty. We analyze when this uncertainty is consequential enough to warrant methodological solutions (Bayesian model averaging over competing causal structures). Focusing on bivariate relationships ($X \rightarrow Y$ vs. $X \leftarrow Y$), we establish that model averaging is beneficial when: (1) structural uncertainty is moderate to high, (2) causal effects differ substantially between structures, and (3) loss functions are sufficiently sensitive to the size of the causal effect. We prove optimality results of our suggested methodological solution under regularity conditions and demonstrate through simulations that modern causal discovery methods can provide, within limits, the necessary quantification. Our framework complements existing robust causal inference approaches by addressing a distinct source of uncertainty typically overlooked in practice.
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